
Resource allocation is an important task to ensure optimal resource usage for FLchain data training. blackMost of the state-of-the art techniques [1]}, [2]} use DRL to implement resource allocation strategies for FLchain systems under energy and channel bandwidth availability conditions.

Lightweight blockchain platforms have been adopted based on DPoS consensus to support model updating and block mining in FLchain [3]}, which has the potential to mitigate energy consumption for FL training.

Some possible directions can be considered to further improve resource allocation, by jointly considering block generation [4]}, model arrival rate, and on-device learning rate. In this way, we can achieve cooperative resource allocation solutions for both miners and local devices in FL-based intelligent edge networks.

